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Found 6 papers, 3 papers with code
NIMS-OS: An automation software to implement a closed loop between artificial intelligence and robotic experiments in materials science
Newly created modules for AI and robotic experiments can be added easily to extend the functionality of the system.
Black-box optimization for integer-variable problems using Ising machines and factorization machines
Ising machines are useful for binary optimization problems because variables can be represented by a single binary variable of Ising machines.
Continuous black-box optimization with quantum annealing and random subspace coding
As QA specializes in optimization of binary problems, a continuous vector has to be encoded to binary, and the solution of QA has to be translated back.
Data-driven determination of the spin Hamiltonian parameters and their uncertainties: The case of the zigzag-chain compound KCu$_4$P$_3$O$_{12}$
We propose a data-driven technique to estimate the spin Hamiltonian, including uncertainty, from multiple physical quantities.
Leveraging Legacy Data to Accelerate Materials Design via Preference Learning
Machine learning applications in materials science are often hampered by shortage of experimental data.
Efficient Construction Method for Phase Diagrams Using Uncertainty Sampling
Furthermore, we show that using the US approach, undetected new phase can be rapidly found, and smaller number of initial sampling points are sufficient.
Materials Science Computational Physics
